A huge moment

Below is footage of IBM’s supercomputer Watson taking on human challengers in Jeopardy. It is amazing, and I predict this will mark a huge turning point for humanity; it will be when the believability of artificial intelligence becomes mainstream.

I have to confess that I have always been bored by discussion about AI and singularity from Robin Hanson and others. I couldn’t get interested in debates about whether AI would be like this or that, or whether it would… um… see, it’s never even interesting enough for me to read far enough past “AI will be…” to even tell what it is they’re discussing about it.

This doesn’t say anything about the topic or Robin’s presentation of it, but simply reflects my own shortcomings in caring about something which seems, to put a Hansonian point on it, so far away. I’ve always heard that AI will likely happen and that it’s just a matter of time. I had no reason to doubt this, but still, it didn’t feel true. This, I know, is not an mindset to be proud of, but I think I’m probably close to the median on this. As in most things, I’d venture I’m far closer to the median than Robin anyway.

But now, with the spectacle of Watson, and after seeing this short clip of him, it changes things. AI and singularity suddenly feel near enough to care about. It feels believable, and so it feels suddenly much more important. Today for the first time ever I could really picture, in a near rather than far sense, all of my skills being replaced by a computer and myself arriving at near zero marginal productivity. I imagine it’s similar to what it would be like to grow up in a pre-flight age, and to be told that human flight is a scientific possibility. Sure you may factually accept the science behind the claims, but until you see it at least almost happen, it’s not nearly real.

I probably should have cared about this sooner, and there is a lesson here about thinking about and caring about things your gut tells you are far away but the facts tell are closer than they feel. I guess that’s why it’s important to force yourself to read Robin Hanson even if your gut tells you the topic feels boring and implausibly far. Maybe this means I need to start caring about cryogenics.

This is certainly going to change how people think about AI, and I’m beginning to wonder if Ken Jennings versus Watson will be the most watched television event of all time.

14 comments

Keep in mind that Watson has been built for a singular task, and unlike the human players he doesn’t have to comprehend a universal of verbal and visual cues. He gets “direct feed” from the video system, and Jeopardy questions have a certain consistent structure to them.

I think the more practical near future of AI is to create “super tools” — programs that are exceptionally good at a particular task, like Watson and Deep Blue. It will be a very long time before a computer program reaches the generalization threshold, where it can accept an arbitrary plain-language query and consistently generate good results.

Another interesting example of “super tool” AI: see the application “Shazam” on the iPhone.

If you want a good example of where “general purpose” AI is now, try http://www.wolframalpha.com. Give it a question like “What is the volume of the earth in gallons?”, it will parse the language and produce a correct result. Same with “How many murders in New York City in 2005?”

Give it a question like “What is the volume of water in 0.25 inches of rainfall over 100 square miles?”, and it fails utterly. It gets pieces right, but can’t parse the meaning. Similarly, “How many murders per capita in New York City in 2005?” fails, even though the software has some understanding of per capita.

Rick, I agree with your thoughts completely. Seems like AI will be used, at least for the near future, to accomplish specific tasks although that will be a welcome form of progress. AI does seem to always come on slower than we hope but it is very interesting whenever we see examples of it in society. Without a doubt, robots and AI are among the most fascinating topics in society. – Adrian Meli

“AI,” if you’ll pardon the term, is a fuzzy concept. What we generally think of as AI – sapient machines – is, absent magic bolts of lightning or Blue Fairy interference, nowhere on the foreseeable horizon. The Turing Test will remain unpassable for Watson and his descendants for quite some time. The theory, so beloved of Heinlein and other Golden Age science fiction writers, that once a machine has some magic number of processors/amount of memory/number of “interconnections” it will just “wake up,” is not based in any real-world phenomenon. There are computers on the planet now which exceed the human brain in any physical information capacity parameter you would care to name (a friend of mine is in charge of one of them: it is truly an impressive hunk of hardware.) They remain quite safely asleep.

As the other commenter points out, what they’ve decided to do instead is make supertools which, for purposes of their use, might as well be intelligent. But nobody is going to run Quicken Family Lawyer on a teraflop machine and replace me, and nobody is going to feed The Wealth of Nations and The General Theory of Money and Interest into Watson and replace you. Chillax, as the young kids say.

This is just an extremely large factual database lookup mechanism. The lookup is narrowed by the category. Then keywords in the question along with context are used to get a subset of possible answers and a very large parallel processing engine can assign percentage matches too them and pick the best match. Obviously the contextual grammer parsing is a little tricky but having thousands upon thousands of jeopardy shows to find the patterns makes it a much more simple problem to solve.

With both Watson and Deep Blue what IBM is proving is there are some tasks that humans do that don’t actually have much to do with intelligence but rather with pattern recognition (chess) and factual database storage and lookup (jeopardy). Computers are good at solving these problems. Always have been. It’s just that it takes very fast hardware and a very smart programmer to make them good enough to solve those problems when they are going against the best humans and with very large datasets that they have to churn through (chess, jeopardy).

These are just what we have termed “smart systems” which are just large pattern recognition and data lookup systems.

There are many instances where google’s behavior already mimics what Watson is doing, its just that Watson has been programmed more specifically to be better at it for a specific subset and kind of inquiries.

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